CAJ | 학술논문

对棉花单叶黄萎病病情严重度与原始及一阶微分光谱反射率、高光谱特征参数进行相关分析,构建病情严重度反演模型.结果表明:可见光和短波红外波段光谱反射率随病情严重度增加而增大,且可见光波段光谱反射率差异比短波红外波段更为显著.以红边面积为自变量的线性模型(r=0.669 6)及以波长694nm处原始光谱反射率为自变量的对数模型(r=0.679 4)均能较好反演病情严重度.通过模型精度检验发现,以714 nm处一阶微分光谱反射率为自变量的线性模型为病情严重度诊断的最佳模型,即y=-282.3x+3.811 2,该模型具有最大相关系数(拟合r=0.699 2,预测r=0.941 0),最小均方根误差(0.257 1)和相对误差(12.74%).文章结果对深入研究棉花黄萎病遥感监测机理提供了理论依据,对利用高光谱遥感数据获取病害信息具有重要应用价值.
대면화단협황위병병정엄중도여원시급일계미분광보반사솔、고광보특정삼수진행상관분석,구건병정엄중도반연모형.결과표명:가견광화단파홍외파단광보반사솔수병정엄중도증가이증대,차가견광파단광보반사솔차이비단파홍외파단경위현저.이홍변면적위자변량적선성모형(r=0.669 6)급이파장694nm처원시광보반사솔위자변량적대수모형(r=0.679 4)균능교호반연병정엄중도.통과모형정도검험발현,이714 nm처일계미분광보반사솔위자변량적선성모형위병정엄중도진단적최가모형,즉y=-282.3x+3.811 2,해모형구유최대상관계수(의합r=0.699 2,예측r=0.941 0),최소균방근오차(0.257 1)화상대오차(12.74%).문장결과대심입연구면화황위병요감감측궤리제공료이론의거,대이용고광보요감수거획취병해신식구유중요응용개치.
The correlation of cotton leaf verticillium wilt severity level with raw hyperspectral reflectance, first derivative hyper-spectral reflectance, and hyperspectral characteristic parameters was analyzed. Using linear and nonlinear regression methods, the hyperspectral remote sensing retrieval models of verticillium wilt severity level with remote sensing parameters as independ-ent variables were constructed and validated. The result showed that spectral reflectance increased significantly in visible and short infrared wave band with the increase in the severity level, and this is especially significant in visible band. The raw spectral reflectance has the maximum coefficient of determination at 694 ran (R~2 =0. 461 6) with severity level and the logarithm model constructed with reflectance at this point is the better one as compared to linear model By the precision evaluation of retrieval models, the linear model with the first derivative reflectance at 717 nm as independent variable was proved to be the best, with R~2 =0. 488 9, RMSE=0. 257 1, and relative error= 12. 74%, for the estimation of verticllium wilt severity level of cotton leaf. The results provide a good basis for further studying monitoring mechanism of cotton verticillium wilt by remote sensing data, and have an important application in acquiring cotton disease information using hyperspectral remote sensing.